1,720,971 research outputs found
Automatic Detection and Categorization of Election-Related Tweets
Get PDFWith the rise in popularity of public social media and micro-blogging services, most notably Twitter, the people have found a venue to hear and be heard by their peers without an intermediary. As a consequence, and aided by the public nature of Twitter, political scientists now potentially have the means to analyse and understand the narratives that organically form, spread and decline among the public in a political campaign.However, the volume and diversity of the conversation on Twitter, combined with its noisy and idiosyncratic nature, make this a hard task. Thus, advanced data mining and language processing techniques are required to process and analyse the data. In this paper, we present and evaluate a technical framework, based on recent advances in deep neural networks, for identifying and analysing election-related conversation on Twitter on a continuous, longitudinal basis. Our models can detect election-related tweets with an F-score of 0.92 and can categorize these tweets into 22 topics with an F-score of 0.90
Mapping Twitter Conversation Landscapes
Get PDFWhile the most ambitious polls are based on standardized interviews
with a few thousand people, millions are tweeting freely and publicly in their own voices about issues they care about. This data offers a vibrant 24/7 snapshot of people’s response to various events and topics. The sheer scale of the data on Twitter allows us to measure in aggregate how the
various issues are rising and falling in prominence over time. However, the volume of the data also means that an intelligent tool is required to allow the users to make sense of the data. To this end, we built a novel, interactive web-based tool for mapping the conversation landscapes on Twitter. Our system utilizes recent advances in natural language processing and deep neural networks that are robust with respect to the noisy and unconventional nature of tweets, in conjunction with a scalable clustering algorithm an interactive visualization engine to allow users to tap the mine of information that is Twitter. We ran a user study with 40 participants using tweets about the 2016 US presidential election and the summer 2016 Orlando shooting, demonstrating that compared to more conventional methods, our tool can increase the speed and the accuracy with which users can identify and make sense of the various conversation topics on Twitter
Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder
Get PDFWe present Tweet2Vec, a novel method for generating general- purpose vector representation of tweets. The model learns tweet embeddings using character-level CNN-LSTM encoder-decoder. We trained our model on 3 million, randomly selected English-language tweets. The model was evaluated using two methods: tweet semantic similarity and tweet sentiment categorization, outperforming the previous state-of-the-art in both tasks. The evaluations demonstrate the power of the tweet embeddings generated by our model for various tweet categorization tasks. The vector representations generated by our model are generic, and hence can be applied to a variety of tasks. Though the model presented in this paper is trained on English-language tweets, the method presented can be used to learn tweet embeddings for different languages
DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs
No full textThis paper describes our approach for the Detecting Stance in Tweets task (SemEval-2016 Task 6). We utilized recent advances in short text categorization using deep learning to create word-level and character-level models. The choice between word-level and character level models in each particular case was informed through validation performance. Our final system is a combination of classifiers using word-level or character-level models. We also employed novel data augmentation techniques to expand and diversify our training dataset, thus making our system more robust. Our system achieved a macro-average precision, recall and F1-scores of 0.67, 0.61 and 0.635 respectively
TweetVista: An AI-Powered Interactive Tool for Exploring Conversations on Twitter
Get PDFWe present TweetVista, an interactive web-based tool for mapping the conversation landscapes on Twitter. TweetVista is an intelligent and interactive desktop web application for exploring the conversation landscapes on Twitter. Given a dataset of tweets, the tool uses advanced NLP techniques using deep neural networks and a scalable clustering algorithm to map out coherent conversation clusters. The interactive visualization engine then enables the users to explore these clusters. We ran three case studies using datasets about the 2016 US presidential election and the summer 2016 Orlando shooting. Despite the enormous size of these datasets, using TweetVista users were able to quickly and clearly make sense of the various conversation topics around these datasets
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Automatic identification of representative content on Twitter
No full textThesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2016.Cataloged from PDF version of thesis.Includes bibliographical references (pages 97-103).Microblogging services, most notably Twitter, have become popular avenues to voice opinions and be active participants of discourse on a wide range of topics. As a consequence, Twitter has become an important part of the political battleground that journalists and political analysts can harness to analyze and understand the narratives that organically form, spread and decline among the public in a political campaign. A challenge with social media is that important discussions around certain issues can be overpowered by majoritarian or controversial topics that provoke strong reactions and attract large audiences. In this thesis we develop a method to identify the specific ideas and sentiments that represent the overall conversation surrounding a topic or event as reflected in collections of tweets. We have developed this method in the context of the 2016 US presidential elections. We present and evaluate a large scale data analytics framework, based on recent advances in deep neural networks, for identifying and analyzing election- related conversation on Twitter on a continuous, longitudinal basis in order to identify representative tweets across prominent election issues. The framework consists of two main components, (1) a dynamic topic model that identifies all tweets related to election issues using knowledge from news stories and continuous learning of Twitter's evolving vocabulary, (2) a semantic model of tweets called Tweet2vec that generates general purpose tweet embeddings used for identifying representative tweets by robust semantic clustering. The topic model performed with an average F-1 score of 0.90 across 22 different election topics on a manually annotated dataset. Tweet2Vec outperformed state-of-the- art algorithms on widely used semantic relatedness and sentiment classification evaluation tasks. To demonstrate the value of the framework, we analyzed tweets leading up to a primary debate and contrasted the automatically identified representative tweets with those that were actually used in the debate. The system was able to identify tweets that represented more semantically diverse conversations around each of the major election issues, in comparison to those that were presented during the debate. This framework may have a broad range of applications, from enabling exemplar-based methods for understanding the gist of large collections of tweets, extensible perhaps to other forms of short text documents, to providing an input for new forms of data-grounded journalism and debate.by Prashanth Vijayaraghavan.S.M
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